OpenRaiser/Intern-Atlas huggingface dataset:
Multi Modal Face Recognition Using Block Based Curvelet Features
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.
An Abductive Framework for Horn Knowledge Base Dynamics
Venue: arXiv cs.LO 2014
Year: 2014

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. We introduced the Horn knowledge base dynamics to deal with two important points: first, to handle belief states that need not be deductively closed; and the second point is the ability to declare certain parts of the belief as immutable. In this paper, we address another, radically new approach to this problem. This approach is very close to the Hansson's dyadic representation of belief. Here, we consider the immutable part as defining a new logical system. By a logical system, we mean that it defines its own consequence relation and closure operator. Based on this, we provide an abductive framework for Horn knowledge base dynamics.
Customisable Handling of Java References in Prolog Programs
Venue: arXiv cs.PL 2014
Year: 2014

Integration techniques for combining programs written in distinct language paradigms facilitate the implementation of specialised modules in the best language for their task. In the case of Java-Prolog integration, a known problem is the proper representation of references to Java objects on the Prolog side. To solve it adequately, multiple dimensions should be considered, including reference representation, opacity of the representation, identity preservation, reference life span, and scope of the inter-language conversion policies. This paper presents an approach that addresses all these dimensions, generalising and building on existing representation patterns of foreign references in Prolog, and taking inspiration from similar inter-language representation techniques found in other domains. Our approach maximises portability by making few assumptions about the Prolog engine interacting with Java (e.g., embedded or executed as an external process). We validate our work by extending JPC, an open-source integration library, with features supporting our approach. Our JPC library is currently compatible with three different open source Prolog engines (SWI, YAP} and XSB) by means of drivers. To appear in Theory and Practice of Logic Programming (TPLP).
On the Effect of Primary User Traffic on Secondary Throughput and Outage Probability Under Rayleigh Flat Fading Channel
Venue: arXiv cs.IT 2014
Year: 2014

The effect of the primary traffic on a secondary user's (SU) throughput under Rayleigh flat fading channel is investigated. For this case, closed form expressions are derived for the average probability of detection and the average probability of false alarm. Based on these expressions, the average SU throughput under the desired signal-to-noise ratio (SNR) constraint in order to maintain the quality of the secondary link is found analytically considering the random arrival or departure of the primary user. It is shown that the spectrum sensing performance and SU throughput degrade with increase in the primary traffic and the deep fade condition of the channel over which the detection is performed. The degree of degradation in SU throughput is seen to be severed further due to the interference link from the primary transmitter to the secondary receiver. Under these detrimental effects, a sensing-throughput trade-off for SU is illustrated. Finally, the combined effect of the primary traffic, fading, imperfect spectrum sensing and the interference link from a primary transmitter is studied on the outage probability at SU.
Analysis of Asymptotically Optimal Sampling-based Motion Planning Algorithms for Lipschitz Continuous Dynamical Systems
Venue: arXiv cs.RO 2014
Year: 2014

Over the last 20 years significant effort has been dedicated to the development of sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRT) and its asymptotically optimal version (e.g. RRT*). However, asymptotic optimality for RRT* only holds for linear and fully actuated systems or for a small number of non-linear systems (e.g. Dubin's car) for which a steering function is available. The purpose of this paper is to show that asymptotically optimal motion planning for dynamical systems with differential constraints can be achieved without the use of a steering function. We develop a novel analysis on sampling-based planning algorithms that sample the control space. This analysis demonstrated that asymptotically optimal path planning for any Lipschitz continuous dynamical system can be achieved by sampling the control space directly. We also determine theoretical bounds on the convergence rates for this class of algorithms. As the number of iterations increases, the trajectory generated by these algorithms, approaches the optimal control trajectory, with probability one. Simulation results are promising.
Weakly Secure Regenerating Codes for Distributed Storage
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of secure distributed data storage under the paradigm of \emph{weak security}, in which no \emph{meaningful information} is leaked to the eavesdropper. More specifically, the eavesdropper cannot get any information about any individual message file or a small group of files. The key benefit of the weak security paradigm is that it incurs no loss in the storage capacity, which makes it practically appealing. In this paper, we present a coding scheme, using a coset coding based outer code and a Product-Matrix Minimum Bandwidth Regenerating code (proposed by Rashmi et al.) as an inner code, that achieves weak security when the eavesdropper can observe any single storage node. We show that the proposed construction has good security properties and requires small finite field size.
Load Frequency Control For Distributed Grid Power System Single Area & Multi-area System
Venue: arXiv cs.SY 2014
Year: 2014

This project presents decentralized control scheme for Load-Frequency Control in power System. In this era renewable energy is most promising solution to man's ever increasing energy needs. But the power production by these resources cannot be controlled unlike in thermal plants. A number of optimal control techniques are adopted to implement a reliable stabilizing controller. It is necessary to interconnect more distributed generation in power systems because of environmental concerns. Primary concern includes global environmental and energy depletion problem. A serious attempt has been undertaken aiming a investigating the load frequency control problem in a power system consisting of two power generation unit and multiple variable load units. The robustness and reliability of the various control schemes is examined through simulations. A system involving thermal plants and a hydro plant is modeled using MAT LAB 2011b.
Resource-Aware Programming for Robotic Vision
Venue: arXiv cs.CV 2014
Year: 2014

Humanoid robots are designed to operate in human centered environments. They face changing, dynamic environments in which they need to fulfill a multitude of challenging tasks. Such tasks differ in complexity, resource requirements, and execution time. Latest computer architectures of humanoid robots consist of several industrial PCs containing single- or dual-core processors. According to the SIA roadmap for semiconductors, many-core chips with hundreds to thousands of cores are expected to be available in the next decade. Utilizing the full power of a chip with huge amounts of resources requires new computing paradigms and methodologies. In this paper, we analyze a resource-aware computing methodology named Invasive Computing, to address these challenges. The benefits and limitations of the new programming model is analyzed using two widely used computer vision algorithms, the Harris Corner detector and SIFT (Scale Invariant Feature Transform) feature matching. The result indicate that the new programming model together with the extensions within the application layer, makes them highly adaptable; leading to better quality in the results obtained.
Towards Cross-layer Reliability Analysis of Transient and Permanent Faults
Venue: arXiv cs.DC 2014
Year: 2014

Due to the increasing complexity of Multi-Processor Systems on Chip (MPSoCs), system-level design methodologies have got a lot of attention in recent years. However, the significant gap between the system-level reliability analysis and the level where the actual faults occur necessitates a cross-layer approach in which the sufficient data about the effects of faults at low levels are passed to the system level. So far, the cross-layer reliability analysis techniques focus on a specific type of faults, e.g., either permanent or transient faults. In this work, we aim at proposing a cross-layer reliability analysis which considers different fault types concurrently and connects reliability analysis techniques at different levels of abstraction using adapters.
Characterizing the Topography of Multi-dimensional Energy Landscapes
Venue: arXiv cs.NA 2014
Year: 2014

A basic issue in optimization, inverse theory,neural networks, computational chemistry and many other problems is the geometrical characterization of high dimensional functions. In inverse calculations one aims to characterize the set of models that fit the data (among other constraints). If the data misfit function is unimodal then one can find its peak by local optimization methods and characterize its width (related to the range of data-fitting models) by estimating derivatives at this peak. On the other hand, if there are local extrema, then a number of interesting and difficult problems arise. Are the local extrema important compared to the global or can they be eliminated (e.g., by smoothing) without significant loss of information? Is there a sufficiently small number of local extrema that they can be enumerated via local optimization? What are the basins of attraction of these local extrema? Can two extrema be joined by a path that never goes uphill? Can the whole problem be reduced to one of enumerating the local extrema and their basins of attraction? For locally ill-conditioned functions, premature convergence of local optimization can be confused with the presence of local extrema. Addressing any of these issues requires topographic information about the functions under study. But in many applications these functions may have hundreds or thousands of variables and can only be evaluated pointwise (by some numerical method for instance). In this paper we describe systematic (but generic) methods of analysing the topography of high dimensional functions using local optimization methods applied to randomly chosen starting models. We provide a number of quantitative measures of function topography that have proven to be useful in practical problems along with error estimates.
Multicell Coordinated Beamforming with Rate Outage Constraint--Part II: Efficient Approximation Algorithms
Venue: arXiv cs.IT 2014
Year: 2014

This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution information is known to the transmitters. The problem under consideration is a probabilistically constrained optimization problem which maximizes a predefined system utility subject to constraints on rate outage probability and power budget of each transmitter. Our recent analysis has shown that the outage-constrained CoBF problem is intricately difficult, e.g., NP-hard. Therefore, the focus of this paper is on suboptimal but computationally efficient algorithms. Specifically, by leveraging on the block successive upper bound minimization (BSUM) method in optimization, we propose a Gauss-Seidel type algorithm, called distributed BSUM algorithm, which can handle differentiable, monotone and concave system utilities. By exploiting a weighted minimum mean-square error (WMMSE) reformulation, we further propose a Jocobi-type algorithm, called distributed WMMSE algorithm, which can optimize the weighted sum rate utility in a fully parallel manner. To provide a performance benchmark, a relaxed approximation method based on polyblock outer approximation is also proposed. Simulation results show that the proposed algorithms are significantly superior to the existing successive convex approximation method in both performance and computational efficiency, and can yield promising approximation performance.
Compositional properties of crypto-based components
Venue: arXiv cs.CR 2014
Year: 2014

This paper presents an Isabelle/HOL+Isar set of theories which allows to specify crypto-based components and to verify their composition properties wrt. cryptographic aspects. We introduce a formalisation of the security property of data secrecy, the corresponding definitions and proofs.
Formalisation and Analysis of Component Dependencies
Venue: arXiv cs.SE 2014
Year: 2014

This set of theories presents a formalisation in Isabelle/HOL+Isar of data dependencies between components. The approach allows to analyse system structure oriented towards efficient checking of system: it aims at elaborating for a concrete system, which parts of the system (or system model) are necessary to check a given property.
Gait trajectory generation for a five link bipedal robot based on a reduced dynamical model
Venue: arXiv cs.RO 2014
Year: 2014

In this paper, a simple trajectory generation method for biped walking is proposed. The dynamic model of the five link bipedal robot is first reduced using several biologically inspired assumptions. A sinusoidal curve is then imposed to the ankle of the swing leg's trajectory. The reduced model is finally obtained and solved: it is an homogeneous second order differential equations with constant coefficients. The algebraic solution obtained ensures a stable rhythmic gait for the bipedal robot. It's continuous in the defined time interval, easy to implement when the boundary conditions are well defined.
Impact of Directional Receiving Antennas on Wireless Networks
Venue: arXiv cs.IT 2014
Year: 2014

We are interested in high data rates internet access, by the mean of LTE based wireless networks. In the aim to improve performance of wireless networks, we propose an approach focused on the use of UE equipped by directional receiving antennas. Indeed, these antennas allow to mitigate the interference and to improve the link budget. Therefore, the Signal to Interference plus Noise Ratio (SINR) can be improved, and consequently the performance and quality of service (QoS), too. We establish the analytical expression of the SINR reached by a user with directional antenna, whatever its location. This expression shows that directional antennas allow an improvement of the SINR, and to quantify it. We develop different scenarios to compare the use of directional antennas instead of omnidirectional ones. They allow to quantify the impact of directional antennas in terms of performance and QoS.
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
Venue: arXiv cs.MM 2014
Year: 2014

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.
An Intelligent Pixel Replication Technique by Binary Decomposition for Digital Image Zooming
Venue: arXiv cs.CV 2014
Year: 2014

Image zooming is the process of enlarging the spatial resolution of a given digital image. We present a novel technique that intelligently modifies the classical pixel replication method for zooming. Our method decomposes a given image into layer of binary images, interpolates them by magnifying the binary patterns preserving their geometric shape and finally aggregates them all to obtain the zoomed image. Although the quality of our zoomed images is much higher than that of nearest neighbor and bilinear interpolation and comparable with bicubic interpolation, the running time of our technique is extremely fast like nearest neighbor interpolation and much faster than bilinear and bicubic interpolation.
A Grammatical Approach to Data-centric Case Management in a Distributed Collaborative Environment
Venue: arXiv cs.DC 2014
Year: 2014

This paper presents a purely declarative approach to artifact-centric case management systems, and a decentralization scheme for this model. Each case is presented as a tree-like structure; nodes bear information that combines data and computations. Each node belongs to a given stakeholder, and semantic rules govern the evolution of the tree structure, as well as how data values derive from information stemming from the context of the node. Stakeholders communicate through asynchronous message passing without shared memory, enabling convenient distribution.
A monolithic multi-time-step computational framework for first-order transient systems with disparate scales
Venue: arXiv cs.NA 2014
Year: 2014

Developing robust simulation tools for problems involving multiple mathematical scales has been a subject of great interest in computational mathematics and engineering. A desirable feature to have in a numerical formulation for multiscale transient problems is to be able to employ different time-steps (multi-time-step coupling), and different time integrators and different numerical formulations (mixed methods) in different regions of the computational domain. We present two new monolithic multi-time-step mixed coupling methods for first-order transient systems. We shall employ unsteady advection-diffusion-reaction equation with linear decay as the model problem, which offers several unique challenges in terms of non-self-adjoint spatial operator and rich features in the solutions. We shall employ the dual Schur domain decomposition technique to handle the decomposition of domain into subdomains. Two different methods of enforcing compatibility along the subdomain interface will be used in the time discrete setting. A systematic theoretical analysis (which includes numerical stability, influence of perturbations, bounds on drift along the subdomain interface) will be performed. The first coupling method ensures that there is no drift along the subdomain interface but does not facilitate explicit/implicit coupling. The second coupling method allows explicit/implicit coupling with controlled (but non-zero) drift in the solution along the subdomain interface. Several canonical problems will be solved to numerically verify the theoretical predictions, and to illustrate the overall performance of the proposed coupling methods. Finally, we shall illustrate the robustness of the proposed coupling methods using a multi-time-step transient simulation of a fast bimolecular advective-diffusive-reactive system.
Exact Recovery in the Stochastic Block Model
Venue: arXiv cs.SI 2014
Year: 2014

The stochastic block model (SBM) with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behaviour. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability $p$ within clusters and $q$ across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower-bounds on the scaling of $|p-q|$ to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if $α=pn/\log(n)$ and $β=qn/\log(n)$ are constant (with $α>β$), recovering the communities with high probability is possible if $\frac{α+β}{2} - \sqrt{αβ}>1$ and impossible if $\frac{α+β}{2} - \sqrt{αβ}<1$. In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst-case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest it may achieve the threshold. An efficient algorithm which succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure.
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
Venue: arXiv cs.CL 2014
Year: 2014

Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.
New Coherence and RIP Analysis for Weak Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2014
Year: 2014

In this paper we define a new coherence index, named the global 2-coherence, of a given dictionary and study its relationship with the traditional mutual coherence and the restricted isometry constant. By exploring this relationship, we obtain more general results on sparse signal reconstruction using greedy algorithms in the compressive sensing (CS) framework. In particular, we obtain an improved bound over the best known results on the restricted isometry constant for successful recovery of sparse signals using orthogonal matching pursuit (OMP).